def test(
discipline_code=discipline_code,
identification_section=identification_section,
grid_definition_info=grid_definition_info,
grid_definition_template=grid_definition_template,
product_definition_template_number=product_definition_template_number,
product_definition_template=product_definition_template,
data_representation_template_number=data_representation_template_number,
data_representation_template=data_representation_template):
m4_fn = '/home/enso/max_micaps/data/tl.000'
(data, stlon, edlon, stlat, edlat, xnum, ynum, xdelta,
ydelta) = read_micaps_iv(m4_fn)
identification_section[0] = 98
identification_section[5] = 2017
identification_section[6] = 3
identification_section[7] = 2
identification_section[8] = 17
grid_definition_info[1] = xnum * ynum
grid_definition_template[7] = xnum
grid_definition_template[8] = ynum
grid_definition_template[11] = stlat * 1000000
grid_definition_template[12] = (stlon + 360) * 1000000
grid_definition_template[14] = edlat * 1000000
grid_definition_template[15] = (edlon + 360) * 1000000
grid_definition_template[16] = xdelta * 1000000
grid_definition_template[17] = ydelta * 1000000
product_definition_template[0] = 0
product_definition_template[1] = 4
product_definition_template[8] = 0
product_definition_template[9] = 103
product_definition_template[10] = 0
product_definition_template[11] = 2
(identification_section, grid_definition_info, grid_definition_template,
product_definition_template, data_representation_template) = [
np.array(_, dtype=np.int32) for _ in [
identification_section, grid_definition_info,
grid_definition_template, product_definition_template,
data_representation_template
]
]
fn = "201703021700_MICAPS-th_CHINA_27km"
f = open(fn + '.grib2', 'wb')
grbo = Grib2Encode(discipline_code, identification_section)
grbo.addgrid(grid_definition_info, grid_definition_template)
grbo.addfield(product_definition_template_number,
product_definition_template,
data_representation_template_number,
data_representation_template, data)
grbo.end()
f.write(grbo.msg)
f.close()
def encode_grib2_data(self):
"""
Encodes member percentile data to GRIB2 format.
Returns:
Series of GRIB2 messages
"""
lscale = 1e6
grib_id_start = [7, 0, 14, 14, 2]
gdsinfo = np.array([0, np.product(self.data.shape[-2:]), 0, 0, 30], dtype=np.int32)
lon_0 = self.proj_dict["lon_0"]
sw_lon = self.grid_dict["sw_lon"]
if lon_0 < 0:
lon_0 += 360
if sw_lon < 0:
sw_lon += 360
gdtmp1 = [1, 0, self.proj_dict['a'], 0, float(self.proj_dict['a']), 0, float(self.proj_dict['b']),
self.data.shape[-1], self.data.shape[-2], self.grid_dict["sw_lat"] * lscale,
sw_lon * lscale, 0, self.proj_dict["lat_0"] * lscale,
lon_0 * lscale,
self.grid_dict["dx"] * 1e3, self.grid_dict["dy"] * 1e3, 0b00000000, 0b01000000,
self.proj_dict["lat_1"] * lscale,
self.proj_dict["lat_2"] * lscale, -90 * lscale, 0]
pdtmp1 = np.array([1, # parameter category Moisture
31, # parameter number Hail
4, # Type of generating process Ensemble Forecast
0, # Background generating process identifier
31, # Generating process or model from NCEP
0, # Hours after reference time data cutoff
0, # Minutes after reference time data cutoff
1, # Forecast time units Hours
0, # Forecast time
1, # Type of first fixed surface Ground
1, # Scale value of first fixed surface
0, # Value of first fixed surface
1, # Type of second fixed surface
1, # Scale value of 2nd fixed surface
0, # Value of 2nd fixed surface
0, # Derived forecast type
1 # Number of ensemble members
], dtype=np.int32)
grib_objects = pd.Series(index=self.times, data=[None] * self.times.size, dtype=object)
drtmp1 = np.array([0, 0, 4, 8, 0], dtype=np.int32)
for t, time in enumerate(self.times):
time_list = list(self.run_date.utctimetuple()[0:6])
if grib_objects[time] is None:
grib_objects[time] = Grib2Encode(0, np.array(grib_id_start + time_list + [2, 1], dtype=np.int32))
grib_objects[time].addgrid(gdsinfo, gdtmp1)
pdtmp1[8] = (time.to_pydatetime() - self.run_date).total_seconds() / 3600.0
data = self.data[t] / 1000.0
data[np.isnan(data)] = 0
masked_data = np.ma.array(data, mask=data<=0)
pdtmp1[-2] = 0
grib_objects[time].addfield(1, pdtmp1, 0, drtmp1, masked_data)
return grib_objects
def write_grib2(self, path):
"""
Writes data to grib2 file. Currently, grib codes are set by hand to hail.
Args:
path: Path to directory containing grib2 files.
Returns:
"""
if self.percentile is None:
var_type = "mean"
else:
var_type = "p{0:02d}".format(self.percentile)
lscale = 1e6
grib_id_start = [7, 0, 14, 14, 2]
gdsinfo = np.array([0, np.product(self.data.shape[-2:]), 0, 0, 30], dtype=np.int32)
lon_0 = self.proj_dict["lon_0"]
sw_lon = self.grid_dict["sw_lon"]
if lon_0 < 0:
lon_0 += 360
if sw_lon < 0:
sw_lon += 360
gdtmp1 = np.array([7, 1, self.proj_dict['a'], 1, self.proj_dict['a'], 1, self.proj_dict['b'],
self.data.shape[-2], self.data.shape[-1], self.grid_dict["sw_lat"] * lscale,
sw_lon * lscale, 0, self.proj_dict["lat_0"] * lscale,
lon_0 * lscale,
self.grid_dict["dx"] * 1e3, self.grid_dict["dy"] * 1e3, 0,
self.proj_dict["lat_1"] * lscale,
self.proj_dict["lat_2"] * lscale, 0, 0], dtype=np.int32)
pdtmp1 = np.array([1, 31, 2, 0, 116, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 192, 0, self.data.shape[0]], dtype=np.int32)
for m, member in enumerate(self.members):
pdtmp1[-2] = m
for t, time in enumerate(self.times):
time_list = list(time.utctimetuple()[0:6])
grbe = Grib2Encode(0, np.array(grib_id_start + time_list + [2, 1], dtype=np.int32))
grbe.addgrid(gdsinfo, gdtmp1)
pdtmp1[8] = (time.to_pydatetime() - self.run_date).total_seconds() / 3600.0
drtmp1 = np.array([0, 0, 4, 8, 0], dtype=np.int32)
data = self.data[m, t].astype(np.float32) / 1000.0
masked_data = np.ma.array(data, mask=data <= 0)
grbe.addfield(1, pdtmp1, 0, drtmp1, masked_data)
grbe.end()
filename = path + "{0}_{1}_mlhail_{2}_{3}.grib2".format(self.ensemble_name.replace(" ", "-"), member,
var_type,
time.to_datetime().strftime("%Y%m%d%H%M"))
print("Writing to " + filename)
grib_file = open(filename, "wb")
grib_file.write(grbe.msg)
grib_file.close()
return
def test(
discipline_code=discipline_code,
identification_section=identification_section,
grid_definition_info=grid_definition_info,
grid_definition_template=grid_definition_template,
product_definition_template_number=product_definition_template_number,
product_definition_template=product_definition_template,
data_representation_template_number=data_representation_template_number,
data_representation_template=data_representation_template):
files = os.listdir(inputDir)
files = sorted(files, key=lambda i: int(i.split('_')[1][0:10]))
count = 0
for ff in files:
fn = os.path.join(inputDir, ff)
data = read_wrf(fn)
identification_section[0] = 98
identification_section[5] = 2015
identification_section[6] = 7
identification_section[7] = 2
identification_section[8] = 0
grid_definition_info[1] = 500 * 420
grid_definition_template[7] = 500
grid_definition_template[8] = 420
product_definition_template[0] = 2
product_definition_template[1] = 3
product_definition_template[8] = count
product_definition_template[9] = 103
product_definition_template[10] = 0
product_definition_template[11] = 10
(identification_section, grid_definition_info,
grid_definition_template, product_definition_template,
data_representation_template) = [
np.array(_, dtype=np.int64) for _ in [
identification_section, grid_definition_info,
grid_definition_template, product_definition_template,
data_representation_template
]
]
if count < 10:
tt = '0%s' % count
else:
tt = count
fn = "2015070200%s_WRF-10mVWIND_CHINA_12km" % tt
f = open(fn + '.grib2', 'wb')
grbo = Grib2Encode(discipline_code, identification_section)
grbo.addgrid(grid_definition_info, grid_definition_template)
grbo.addfield(product_definition_template_number,
product_definition_template,
data_representation_template_number,
data_representation_template, data)
grbo.end()
f.write(grbo.msg)
f.close()
count += 1
def test(
discipline_code=discipline_code,
identification_section=identification_section,
grid_definition_info=grid_definition_info,
grid_definition_template=grid_definition_template,
product_definition_template_number=product_definition_template_number,
product_definition_template=product_definition_template,
data_representation_template_number=data_representation_template_number,
data_representation_template=data_representation_template):
files = os.listdir(os.path.join(inputDir, 'ecmwf_thin', 'T', '850'))
for ff in files:
m4_fn = os.path.join(inputDir, 'ecmwf_thin', 'T', '850', ff)
(data, X, Y, year, month, day, hour, prep, xx, yy, stlon, edlon, stlat,
edlat, xnum, ynum, xdelta, ydelta) = read_micaps_iv(m4_fn)
identification_section[0] = 98
identification_section[5] = 2000 + int(year)
identification_section[6] = month
identification_section[7] = day
identification_section[8] = hour
grid_definition_info[1] = xnum * ynum
grid_definition_template[7] = xnum
grid_definition_template[8] = ynum
grid_definition_template[11] = stlat * 1000000
grid_definition_template[12] = (stlon + 360) * 1000000
grid_definition_template[14] = edlat * 1000000
grid_definition_template[15] = (edlon + 360) * 1000000
grid_definition_template[16] = xdelta * 1000000
grid_definition_template[17] = ydelta * 1000000
product_definition_template[0] = 0
product_definition_template[1] = 0
product_definition_template[8] = int(prep)
product_definition_template[9] = 100
product_definition_template[10] = 0
product_definition_template[11] = 85000
(identification_section, grid_definition_info,
grid_definition_template, product_definition_template,
data_representation_template) = [
np.array(_, dtype=np.int32) for _ in [
identification_section, grid_definition_info,
grid_definition_template, product_definition_template,
data_representation_template
]
]
fn = "20%s%s%s0000_EC-850hpa-air-temperature-%sh_CHINA_27km" % (
year, month, day, prep)
f = open(fn + '.grib2', 'wb')
grbo = Grib2Encode(discipline_code, identification_section)
grbo.addgrid(grid_definition_info, grid_definition_template)
grbo.addfield(product_definition_template_number,
product_definition_template,
data_representation_template_number,
data_representation_template, data)
grbo.end()
f.write(grbo.msg)
f.close()
def data2grib_smiple(self,discipline,category,element,statistical,status,year,month,day,hour,minute,second,forecasttime,timerange,field,ngrdpts,leveltype,level,generating_method,istimepoint):
date_time=datetime.datetime(int(year),int(month),int(day),int(hour),int(minute),int(second))#起报时间
'''定义 section0-1
@discipline:Discipline or GRIB Master Table Number (Code Table 0.0).
(0 for meteorlogical, 1 for hydrological, 2 for land surface, 3 for space,
10 for oceanographic products),学科，默认0
@idsect：Sequence containing identification section (section 1)，section1内容
Must have len >= 13.
listsec1[0]=Id of orginating centre (Common Code Table C-1)，中心点，固定值38，北京
listsec1[1]=Id of orginating sub-centre (local table)，子中心点，默认0
listsec1[2]=GRIB Master Tables Version Number (Code Table 1.0)，主表版本号，0：时间点产品，8：时间段产品
listsec1[3]=GRIB Local Tables Version Number (Code Table 1.1)，固定值0，没有本地表
listsec1[4]=Significance of Reference Time (Code Table 1.2)，固定值1，表示起报时间
listsec1[5]=Reference Time - Year (4 digits)
listsec1[6]=Reference Time - Month
listsec1[7]=Reference Time - Day
listsec1[8]=Reference Time - Hour
listsec1[9]=Reference Time - Minute
listsec1[10]=Reference Time - Second
listsec1[11]=Production status of data (Code Table 1.3)，产品状态
表：Status 产品状态定义
VALUE值 REMARKS描述
0 正式产品
1 测试产品
2 科研
3 再分析
4 TIGGE
5 TIGGE测试
listsec1[12]=Type of processed data (Code Table 1.4)，数据类型，固定值1：预报产品
'''
# section1
#discipline = 0 # 学科
#year = 2019
#month = 11
#day = 19
#hour = 20
#minute = 0
#second = 0
#status = 0 # 产品状态
idsect = [38, 0, 0, 0, 1, year, month, day, hour, minute, second, status, 1]
# 初始化Grib2Encode类
ge = Grib2Encode(discipline, idsect)
gdsinfo = [0, ngrdpts, 0, 0, 0]
'''格点定义模板的内容值，默认使用Template3.0
@param gdtmpl: Contains the data values for the specified Grid Definition
Template ( NN=gdsinfo[4] ). Each element of this integer
array contains an entry (in the order specified) of Grid
Definition Template 3.NN，
按照顺序列出Template3.0的值
字节范围 类型 描述 内容
15 CHAR 地球模型参数 固定值为6：地球半径为 6,371,229.0米
16 CHAR 地球模型参数 固定值为0：
17-20 INT 地球模型参数 固定值为0：
21 CHAR 地球模型参数 固定值为0：
22-25 INT 地球模型参数 固定值为0：
26 CHAR 地球模型参数 固定值为0：
27-30 INT 地球模型参数 固定值为0：
31-34 INT 横向格点数 经度方向
35-38 INT 纵向格点数 纬度方向
39-42 INT 基本角度单位 固定值为1
43-46 INT 基本角度份数 固定值为1000000
47-50 INT 纬度起始点位置 值为：起始纬度* 1000000
51-54 INT 经度起始点位置 值为：起始经度* 1000000
55 CHAR 经度和纬度方向的增量信息 值为48
56-59 INT 纬度终止点位置 值为：终止纬度* 1000000
60-63 INT 经度终止点位置 值为：终止纬度* 1000000
64-67 INT 经度方向的格距 值为：经度格距* 1000000
68-71 INT 纬度方向的格距 值为：纬度格距* 1000000
72 CHAR 数据的扫描方向 值64表示：经度方向为从小到大，纬度方向为从小到大
'''
ge.addgrid(gdsinfo, self.gdtmpl)
'''写入section4-7
@pdtnum:Product Definition Template Number
产品模板编号
默认0：使用模板4.0（时间点产品[温度，相对湿度，能见度，风-U,风-V,雾，霾，雷暴等]）
默认8：使用模板4.8（时间段产品[最大温度，最小相对湿度，累积降水等]）
@pdtmpl：Sequence with the data values for the specified Product Definition
Template (N=pdtnum)，对应产品模板的内容，按照顺序排
==================================================================
产品模版4.0 时间点产品
字节范围 类型 描述 内容
10 CHAR 产品的分类 *Note1
11 CHAR 产品的编号 *Note2
12 CHAR 生成方法 2: Forecast预报产品 8：Observation实况产品
13 CHAR 后台生成进程标识 固定值为0
14 CHAR 确定分析和预报生成过程 固定值为0
15-16 SHORT 在起报时间后，需cut-off的数据时间小时部分 固定值为0
17 CHAR 在起报时间后，需cut-off的数据时间分钟部分 固定值为0
18 CHAR 时间范围的单位 固定值为1：1小时
19-22 INT 相对于起报时间，预报时间的时间数
23 CHAR 第一层次类型 *Note3
24 CHAR 第一层次因子 固定值为0
25-28 INT 第一层次值 *Note4
29 CHAR 第二层次类型 固定值为255
30 CHAR 第二层次因子 固定值为0
31-34 INT 第二层次值 固定值为0
=====================================================================
产品模版4.8 时间段产品
字节范围 类型 描述 内容
10 CHAR 产品的分类 *Note1
11 CHAR 产品的编号 *Note2
12 CHAR 生成方法 2: Forecast预报产品
8：Observation实况产品
13 CHAR 后台生成进程标识 固定值为0
14 CHAR 确定分析和预报生成过程 固定值为0
15-16 SHORT 在起报时间后，需cut-off的数据时间小时部分 固定值为0
17 CHAR 在起报时间后，需cut-off的数据时间分钟部分 固定值为0
18 CHAR 时间范围的单位 固定值为1：1小时
19-22 INT 相对于起报时间，预报时间的时间数 *Note6
23 CHAR 第一层次类型 *Note3
24 CHAR 第一层次因子 固定值为0
25-28 INT 第一层次值 *Note4
29 CHAR 第二层次类型 固定值为255
30 CHAR 第二层次因子 固定值为0
31-34 INT 第二层次值 固定值为0
35-36 SHORT 预报结束时间年
37 CHAR 预报结束时间月
38 CHAR 预报结束时间日
39 CHAR 预报结束时间时
40 CHAR 预报结束时间分
41 CHAR 预报结束时间秒
42 CHAR 时间范围的数量 固定为1
43-46 INT 缺测数据总个数
47 CHAR 统计处理方式 *Note5
48 CHAR 时间增量类型 固定值为2：对于同一起报时间的多个连续的预报，预报时间是递增的
49 CHAR 预报时间范围单位 固定为1：小时
50-53 INT 时间范围长度 以49字节为单位
54 CHAR 递增时间单位 固定为1：小时
55-58 INT 连续预报时间段的时间增量
@drtnum：Data Representation Template Number，数据表示段 模板编号
@drtmpl：Sequence with the data values for the specified Data Representation
Template,对应数据段模板的内容，按照顺序排
字节范围 类型 描述 内容
12-15 FLOAT 参考值 9999
16-17 SHORT 二进制比例因子 0
18-19 SHORT 十进制比例因子 0
20 CHAR Simple压码每个包的字节个数 24
21 CHAR 原数据值的类型 固定为0：原始数据为浮点数
默认：drtmpl=[9999,0,0,24,0]
@field：numpy array of data points to pack.
If field is a masked array, then a bitmap is created from
the mask，格点场数据，按先经度后纬度，经向先西后东（从小到大），纬向先南后北（从小到大）顺序装填
@coordlist：Sequence containing floating point values intended to document
the vertical discretization with model data
on hybrid coordinate vertical levels. Default None
'''
# section 4 产品模板定义
#isTimePoint = True # 是否是时间点产品，温度，风，相对湿度，云量等都是时间点的数据，而降水，最高温，最低温，最大相对湿度，最小相对湿度等都是时间段的数据
pdtnum = 0 if istimepoint == True else 8 # 0：时间点产品；8：时间段产品
# 测试平均温度 某时间点的2米温度，level type:103,level:2,产品模板4.0，unit:k,category:0,element:0,statistical:0,status:0
#category = 0
#element = 0
product_type = generating_method # 2:预报产品 8：实况产品 再分析产品=？
#statistical = 0
#time_range = 3 # 相对于起报时间，预报时间的时间数(该模版中的预报时效为每一段的起始预报时间的时间数，如3小时间隔的预报时间的时间数为0，3，6…)
#level_type = 103
#level = 2
#连续预报时间段的时间增量
#time_increment=0
if istimepoint == True:
pdtmpl = [category, element, product_type, 0, 0, 0, 0, 1, forecasttime, leveltype, 0, level, 255, 0, 0]
else:
# 缺测数据总个数，默认0
miss_value_count = 0
# 预报结束时间,根据起报时间+预报时效计算得出，是时间范围
forecast_time = gt.addtime_hour(date_time, forecasttime)
forecast_year = forecast_time.year
forecast_month = forecast_time.month
forecast_day = forecast_time.day
forecast_hour = forecast_time.hour
forecast_minute = forecast_time.minute
forecast_second = forecast_time.second
pdtmpl = [category, element, product_type, 0, 0, 0, 0, 1, forecasttime, leveltype, 0, level, 255, 0, 0,
forecast_year, forecast_month, forecast_day, forecast_hour, forecast_hour, forecast_minute,
forecast_second, 1, miss_value_count, statistical, 2, 1, 49, 1,timerange]
# section 5
drtnum = 0
# section5数据表示段，模板5.0，赋值
drtmpl = [9999, 0, 0, 24, 0]
#drtmpl = [1208536781, -3, 1, 24, 0]
#field = None
ge.addfield(pdtnum, pdtmpl, drtnum, drtmpl, field)
# finalize the grib message.
ge.end()
self.message=ge
def test(
discipline_code = discipline_code,
identification_section = identification_section,
grid_definition_info = grid_definition_info,
grid_definition_template = grid_definition_template,
product_definition_template_number = product_definition_template_number,
product_definition_template = product_definition_template,
data_representation_template_number = data_representation_template_number,
data_representation_template = data_representation_template):
files = os.listdir(os.path.join(inputDir, 'ecmwf_thin', 'dt', '1000', '4'))
files = sorted(files, key=lambda i:-int(i.split('.')[1]))
count = 0
for ff in files:
m4_fn = os.path.join(inputDir, 'ecmwf_thin', 'dt', '1000', '4', ff)
(
data, X, Y, year, month, day, hour, prep, xx, yy,
stlon, edlon, stlat, edlat, xnum, ynum, xdelta, ydelta
) = read_micaps_iv(m4_fn)
identification_section[0] = 98
identification_section[5] = 2000 + int(year)
identification_section[6] = month
identification_section[7] = day
identification_section[8] = hour
grid_definition_info[1] = xnum * ynum
grid_definition_template[7] = xnum
grid_definition_template[8] = ynum
grid_definition_template[11] = stlat * 1000000
grid_definition_template[12] = (stlon+360) * 1000000
grid_definition_template[14] = edlat * 1000000
grid_definition_template[15] = (edlon+360) * 1000000
grid_definition_template[16] = xdelta * 1000000
grid_definition_template[17] = ydelta * 1000000
product_definition_template[0] = 0
product_definition_template[1] = 9
product_definition_template[8] = int(prep)
product_definition_template[9] = 100
product_definition_template[10] = 0
product_definition_template[11] = 1000*100
(
identification_section,
grid_definition_info,
grid_definition_template,
product_definition_template,
data_representation_template
) = [np.array(_, dtype=np.int32) for _ in [
identification_section,
grid_definition_info,
grid_definition_template,
product_definition_template,
data_representation_template
]]
if count < 10:
tt = '0%s' %count
else:
tt =count
if int(hour)-8 == 0:
hh = '00'
else:
hh = int(hour) - 8
fn = "20%s%s%s%s%s_EC-1000hpa24hTemperature_CHINA_27km" %(year, month, day, hh, tt)
f=open(fn + '.grib2','wb')
grbo = Grib2Encode(
discipline_code,
identification_section)
grbo.addgrid(
grid_definition_info,
grid_definition_template)
grbo.addfield(
product_definition_template_number,
product_definition_template,
data_representation_template_number,
data_representation_template,
data)
grbo.end()
f.write(grbo.msg)
f.close()
count +=1
from ncepgrib2 import Grib2Decode, Grib2Encode
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# read soil moisture grib record with GRIB API.
grbs = pygrib.open('../sampledata/gfs.t12z.pgrbf120.2p5deg.grib2')
grbmsg = grbs[208] # soil moisture
data = grbmsg.values
print(data.min(), data.max())
# convert grib message to a ncepgrib2.Grib2Message instance.
grb = Grib2Decode(grbmsg.tostring(), gribmsg=True)
# re-write the grib message to a new file.
f = open('test_masked.grb', 'wb')
grbo = Grib2Encode(grb.discipline_code, grb.identification_section)
grbo.addgrid(grb.grid_definition_info, grb.grid_definition_template)
# add product definition template, data representation template
# and data (including bitmap which is read from data mask).
grbo.addfield(grb.product_definition_template_number,
grb.product_definition_template,
grb.data_representation_template_number,
grb.data_representation_template, data)
# finalize the grib message.
grbo.end()
# write it to the file.
f.write(grbo.msg)
# close the output file
f.close()
# read and plot the data in the new file.
